Performance Evaluation of Automated Flood Inundation Mapping Techniques Using Multi‐Temporal Sentinel‐1 SAR Data

ABSTRACT In recent times, the development of algorithms to delineate water surface maps has significantly boosted flood monitoring and mitigation efforts by utilizing dual polarization, multi‐temporal Sentinel‐1 synthetic aperture radar (SAR) data. The Sentinel‐1 mission, with its global land monitoring capability, has been widely employed for SAR‐based flood mapping. Compared to single‐image flood algorithms, change‐detection methods offer superior results by deriving flood extent from classified changes, requiring data‐based parameterization. This study critically evaluates the effectiveness of three cutting‐edge thresholding algorithms—Edge Otsu, Bmax Otsu, and Kittler–Illingworth (KI)—for automated flood water detection using dual polarization, multi‐temporal Sentinel‐1 SAR data, focusing on the September 2019 flood event in North‐eastern Thailand. Utilizing Google Earth Engine for preprocessing and image correction, the study examines three Sentinel‐1 change detection models—Difference Image, Normalized Difference Flood Index (NDFI), and Normalized Difference Sigma‐naught Index (NDSI). Among 27 combinations of inputs, change detection methods, and thresholding algorithms, the “Harmonic data‐S1GBM (2016–2017)” input paired with the KI thresholding algorithm and the NDSI change detection method achieved the highest overall accuracy of 86.29% (calculated using user accuracy, producer accuracy, and overall accuracy metrics against 2000 validation samples from GISTDA flood maps and Sentinel‐2 NDWI data). This combination proved most effective in distinguishing flooded from non‐flooded areas, underscoring the importance of selecting optimal data inputs and algorithms for accurate flood inundation mapping. The results highlight the superiority of the KI thresholding algorithm, particularly when used with harmonic data inputs, and establish a robust framework for future flood monitoring applications using Sentinel‐1 SAR data. Furthermore, the study emphasizes that for global and automatic flood services, algorithms should not depend on locally optimized parameters, as these cannot be automatically estimated and vary spatially, significantly affecting mapping accuracy.